Using Explainable Deep Learning and Logistic Regression to Evaluate Complementary and Integrative Health Treatments in Patients with Musculoskeletal Disorders

There is an increasing interest in the use of Complementary and Integrative Health (CIH) for treatment of pain as an alternative to opioid medications. We use a novel explainable deep learning approach compared and contrasted to a traditional logistic regression model to explore the impact of musculoskeletal disorder related factors on the use of CIH. The impact scores from the neural network show high correlation with the log-odds ratios of the logistic regression, showing the promise that neural networks can be used to identify high impact factors without depending on a priori assumptions and limitations of traditional statistical models

The effect of simulated narratives that leverage EMR data on shared decision-making: a pilot study

BACKGROUND: Shared decision-making can improve patient satisfaction and outcomes. To participate in shared decision-making, patients need information about the potential risks and benefits of treatment options. Our team has developed a novel prototype tool for shared decision-making called hearts like mine (HLM) that leverages EHR data to provide personalized information to patients regarding potential outcomes of different treatments. These potential outcomes are presented through an Icon array and/or simulated narratives for each “person” in the display. In this pilot project we sought to determine whether the inclusion of simulated narratives in the display affects individuals’ decision-making. Thirty subjects participated in this block-randomized study in which they used a version of HLM with simulated narratives and a version without (or in the opposite order) to make a hypothetical therapeutic decision. After each decision, participants completed a questionnaire that measured decisional confidence. We used Chi square tests to compare decisions across conditions and Mann–Whitney U tests to examine the effects of narratives on decisional confidence. Finally, we calculated the mean of subjects’ post-experiment rating of whether narratives were helpful in their decision-making. RESULTS: In this study, there was no effect of simulated narratives on treatment decisions (decision 1: Chi squared = 0, p = 1.0; decision 2: Chi squared = 0.574, p = 0.44) or Decisional confidence (decision 1, w = 105.5, p = 0.78; decision 2, w = 86.5, p = 0.28). Post-experiment, participants reported that narratives helped them to make decisions (mean = 3.3/4). CONCLUSIONS: We found that simulated narratives had no measurable effect on decisional confidence or decisions and most participants felt that the narratives were helpful to them in making therapeutic decisions. The use of simulated stories holds promise for promoting shared decision-making while minimizing their potential biasing effect

Characterizing nutrient patterns of food items in adolescent diet using data from a novel citizen science project and the US National Health and Nutrition Examination Survey (NHANES)

IntroductionA healthy diet is essential for promoting good health during adolescence and mitigating disease risks in adulthood. This underscores the need for improved nutrition education and increased access to healthier food choices. However, the accuracy of dietary data poses a significant challenge in nutritional research.MethodsWe utilized and analyzed a novel dietary record dataset collected through a high school citizen science project to address this issue. We focused on nutrients rather than food groups to characterize adolescent dietary patterns. The same analyses were performed on the 2019–2021 National Health and Nutrition Examination Survey data for comparison.ResultsBased on the U.S. Food and Drug Administration’s recommended daily value (DV) for nutrients, the majority of food items in our citizen science dataset are low (i.e., <5% DV) in lipids, fiber, potassium, calcium, iron, sugar, and cholesterol. Only a minority of items are high (i.e., >20% DV) in macro and micronutrients. The clustering analysis identified nine food clusters with distinct nutrient profiles that vary significantly in size. The analyses on the NHANES data yielded similar findings, but with higher proportions of foods high in energy, lipids, carbohydrates, sugar, iron, and sodium compared with those of the citizen science dataset.DiscussionThis study demonstrates the potential of citizen science projects in gathering valuable dietary data and understanding adolescent nutrient intake. Identifying critical nutrient gaps can guide targeted nutrition education and the provision of accessible healthier food options, leading to positive health outcomes during adolescence and beyond

Making Primarily Professional Terms More Comprehensible to the Lay Audience

Certain texts, such as clinical reports and clinical trial records, are written by professionals for professionals while being increasingly accessed by lay people. To improve the comprehensibility of such documents to the lay audience, we conducted a pilot study to analyze terms used primarily by health professionals, and explore ways to make them more comprehensible to lay people

Identification and Use of Frailty Indicators from Text to Examine Associations with Clinical Outcomes Among Patients with Heart Failure.

Frailty is an important health outcomes indicator and valuable for guiding healthcare decisions in older adults, but is rarely collected in a quantitative, systematic fashion in routine healthcare. Using a cohort of 12,000 Veterans with heart failure, we investigated the feasibility of topic modeling to identify frailty topics in clinical notes. Topics were generated through unsupervised learning and then manually reviewed by an expert. A total of 53 frailty topics were identified from 100,000 notes. We further examined associations of frailty with age-, sex-, and Charlson Comorbidity Index-adjusted 1-year hospitalizations and mortality (composite outcome) using logistic regression. Frailty (≤ 4 topics versu

Opioid use and opioid use disorder in mono and dual-system users of veteran affairs medical centers

IntroductionEfforts to achieve opioid guideline concordant care may be undermined when patients access multiple opioid prescription sources. Limited data are available on the impact of dual-system sources of care on receipt of opioid medications.ObjectiveWe examined whether dual-system use was associated with increased rates of new opioid prescriptions, continued opioid prescriptions and diagnoses of opioid use disorder (OUD). We hypothesized that dual-system use would be associated with increased odds for each outcome.MethodsThis retrospective cohort study was conducted using Veterans Administration (VA) data from two facilities from 2015 to 2019, and included active patients, defined as Veterans who had at least one encounter in a calendar year (2015–2019). Dual-system use was defined as receipt of VA care as well as VA payment for community care (non-VA) services. Mono users were defined as those who only received VA services. There were 77,225 dual-system users, and 442,824 mono users. Outcomes were three binary measures: new opioid prescription, continued opioid prescription (i.e., received an additional opioid prescription), and OUD diagnosis (during the calendar year). We conducted a multivariate logistic regression accounting for the repeated observations on patient and intra-class correlations within patients.ResultsDual-system users were significantly younger than mono users, more likely to be women, and less likely to report white race. In adjusted models, dual-system users were significantly more likely to receive a new opioid prescription during the observation period [Odds ratio (OR) = 1.85, 95% confidence interval (CI) 1.76–1.93], continue prescriptions (OR = 1.24, CI 1.22–1.27), and to receive an OUD diagnosis (OR = 1.20, CI 1.14–1.27).DiscussionThe prevalence of opioid prescriptions has been declining in the US healthcare systems including VA, yet the prevalence of OUD has not been declining at the same rate. One potential problem is that detailed notes from non-VA visits are not immediately available to VA clinicians, and information about VA care is not readily available to non-VA sources. One implication of our findings is that better health system coordination is needed. Even though care was paid for by the VA and presumably closely monitored, dual-system users were more likely to have new and continued opioid prescriptions

What factors affect patients' recall of general practitioners' advice?

<p>Abstract</p> <p>Background</p> <p>In order for patients to adhere to advice, provided by family doctors, they must be able to recall it afterwards. However, several studies have shown that most patients do not fully understand or memorize it. The aim of this study was to determine the influence of demographic characteristics, education, amount of given advice and the time between consultations on recalled advice.</p> <p>Methods</p> <p>A prospective survey, lasting 30 months, was conducted in an urban family practice in Slovenia. Logistic regression analysis was used to identify the risk factors for poorer recall.</p> <p>Results</p> <p>250 patients (87.7% response rate) received at least one and up to four pieces of advice (2.4 ± 0.8). A follow-up consultation took place at 47.4 ± 35.2 days. The determinants of better recall were high school (OR 0.4, 95% CI 0.15-0.99, p = 0.049) and college education (OR 0.3, 95% CI 0.10-1.00, p = 0.050), while worse recall was determined by number of given instructions three or four (OR 26.1, 95% CI 3.15-215.24, p = 0.002; OR 56.8, 95% CI 5.91-546.12, p < 0.001, respectively) and re-test interval: 15-30 days (OR 3.3, 95% CI 1.06-10.13, p = 0.040), 31-60 days (OR 3.2, 95% CI 1.28-8.07, p = 0.013) and more than 60 days (OR 2.5, 95% CI 1.05-6.02, p = 0.038).</p> <p>Conclusions</p> <p>Education was an important determinant factor and warrants further study. Patients should be given no more than one or two instructions in a consultation. When more is needed, the follow-up should be within the next 14 days, and would be of a greater benefit to higher educated patients.</p

Making effective use of healthcare data using data-to-text technology

Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports or via ehealth platforms by their doctors. Unfortunately, such text is the outcome of a highly labour-intensive process if it is done by healthcare professionals. It is also prone to incompleteness, subjectivity and hard to scale up to different domains, wider audiences and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.Comment: 27 pages, 2 figures, book chapte

Predicting sample size required for classification performance

<p>Abstract</p> <p>Background</p> <p>Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target.</p> <p>Methods</p> <p>We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method.</p> <p>Results</p> <p>A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p < 0.05).</p> <p>Conclusions</p> <p>This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.</p